Almarri Badar, Naveen Kumar Baskaran, Aditya Pai Haradi, Bhatia Khan Surbhi, Asiri Fatima, Mahesh Thyluru Ramakrishna
Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Alhasa, Saudi Arabia.
Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, India.
Front Med (Lausanne). 2024 Oct 21;11:1470941. doi: 10.3389/fmed.2024.1470941. eCollection 2024.
Retinal vessel segmentation is a critical task in fundus image analysis, providing essential insights for diagnosing various retinal diseases. In recent years, deep learning (DL) techniques, particularly Generative Adversarial Networks (GANs), have garnered significant attention for their potential to enhance medical image analysis. This paper presents a novel approach for retinal vessel segmentation by harnessing the capabilities of GANs. Our method, termed GANVesselNet, employs a specialized GAN architecture tailored to the intricacies of retinal vessel structures. In GANVesselNet, a dual-path network architecture is employed, featuring an Auto Encoder-Decoder (AED) pathway and a UNet-inspired pathway. This unique combination enables the network to efficiently capture multi-scale contextual information, improving the accuracy of vessel segmentation. Through extensive experimentation on publicly available retinal datasets, including STARE and DRIVE, GANVesselNet demonstrates remarkable performance compared to traditional methods and state-of-the-art deep learning approaches. The proposed GANVesselNet exhibits superior sensitivity (0.8174), specificity (0.9862), and accuracy (0.9827) in segmenting retinal vessels on the STARE dataset, and achieves commendable results on the DRIVE dataset with sensitivity (0.7834), specificity (0.9846), and accuracy (0.9709). Notably, GANVesselNet achieves remarkable performance on previously unseen data, underscoring its potential for real-world clinical applications. Furthermore, we present qualitative visualizations of the generated vessel segmentations, illustrating the network's proficiency in accurately delineating retinal vessels. In summary, this paper introduces GANVesselNet, a novel and powerful approach for retinal vessel segmentation. By capitalizing on the advanced capabilities of GANs and incorporating a tailored network architecture, GANVesselNet offers a quantum leap in retinal vessel segmentation accuracy, opening new avenues for enhanced fundus image analysis and improved clinical decision-making.
视网膜血管分割是眼底图像分析中的一项关键任务，为诊断各种视网膜疾病提供重要见解。近年来，深度学习（DL）技术，特别是生成对抗网络（GAN），因其在增强医学图像分析方面的潜力而备受关注。本文提出了一种利用GAN能力进行视网膜血管分割的新方法。我们的方法称为GANVesselNet，采用了一种专门针对视网膜血管结构复杂性定制的GAN架构。在GANVesselNet中，采用了双路径网络架构，其特征在于自动编码器-解码器（AED）路径和受UNet启发的路径。这种独特的组合使网络能够有效地捕捉多尺度上下文信息，提高血管分割的准确性。通过在包括STARE和DRIVE在内的公开可用视网膜数据集上进行广泛实验，与传统方法和最新的深度学习方法相比，GANVesselNet表现出卓越的性能。所提出的GANVesselNet在STARE数据集上分割视网膜血管时表现出卓越的灵敏度（0.8174）、特异性（0.9862）和准确性（0.9827），并在DRIVE数据集上取得了值得称赞的结果，灵敏度为（0.7834）、特异性为（0.9846）、准确性为（0.9709）。值得注意的是，GANVesselNet在以前未见过的数据上取得了显著性能，突出了其在实际临床应用中的潜力。此外，我们展示了生成的血管分割的定性可视化结果，说明了网络在准确描绘视网膜血管方面的能力。总之，本文介绍了GANVesselNet，一种用于视网膜血管分割的新颖且强大的方法。通过利用GAN的先进能力并结合定制的网络架构，GANVesselNet在视网膜血管分割准确性方面实现了巨大飞跃，为增强眼底图像分析和改善临床决策开辟了新途径。
